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Activity Number: 74
Type: Contributed
Date/Time: Sunday, July 31, 2016 : 4:00 PM to 5:50 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #320360 View Presentation
Title: Joint Multilevel Gaussian Graphical Model
Author(s): Liang Shan* and Inyoung Kim
Companies: and Virginia Tech
Keywords: gene network ; pathway network ; joint estimation ; heterogeneous classes ; gaussian graphical model

A gene pathway is composed of a series of genes to work together for a particular cellular or physical function. In addition, it has been found that many pathways often work together to accomplish certain tasks. In this paper, we are dealing with gene expression data across heterogeneous classes, with both gene and gene pathway information. We consider that common network structure among classes contains common gene networks within pathways and common gene pathway interactions. We will propose jointly estimating gene networks among classes by taking advantage of their common structure, which is composed of two levels; with one level indicating pathway interaction network and the other level indicating gene network within each pathway. Previous research only concentrate on estimating precision matrices jointly across classes or estimating a multilevel Gaussian graphical model within a certain class, no research so far has integrated multilevel and multiclass Gaussian graphical model.

Authors who are presenting talks have a * after their name.

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